• Journal of Information Processing Systems
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• 제13권2호
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• pp.360-369
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• 2017

In this paper, a new iterative algorithm for reconstructing block sparse signals, called block backtracking-based adaptive orthogonal matching pursuit (BBAOMP) method, is proposed. Compared with existing methods, the BBAOMP method can bring some flexibility between computational complexity and reconstruction property by using the backtracking step. Another outstanding advantage of BBAOMP algorithm is that it can be done without another information of signal sparsity. Several experiments illustrate that the BBAOMP algorithm occupies certain superiority in terms of probability of exact reconstruction and running time.

• 전자공학회논문지S
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• 제34S권6호
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• pp.58-66
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• 1997

This paper proposes a multiresolution surface parameter estimation method for range images. Based on robust estimation of surface parameters, it approximates a patch to a planar surface in the locally adaptive window. Selection of resolution is made pixelwise by comparing a locally computed homogeneity measure with th eglobal threshold determined by te distribution of the approximation error. The proposed multiresolution surface parameter estimation method is applied to range image reconstruction. Computer simulation results with noisy rnag eimages contaminated by additive gaussian noise and impulse noise show that the proposed multiresolution reconstruction method well preserves step and roof edges compared with the conventional methods. Also the segmentation method based on the estimated surface parameters is shown to be robust to noise.

• 한국통신학회논문지
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• 제31권12C호
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• pp.1194-1200
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• 2006

본 논문에서는 국부 통계 특성을 이용한 적응 MAP 방식의 고해상도 영상 복원 알고리즘에 대해 제안한다. 고해상도 원 영상의 윤곽선을 보존하기 위해 저해상도 영상의 국부 특성을 이용하여 시각함수를 정의하였고, MAP(Maximum A Posteriori) 추정 방식을 이용하여 국부적인 열화 정도(smoothness)를 조절하였다. 또한 가중치가 부여된 함수를 이용하여 원 고해상도 영상에 가능한 가까운 최적의 해를 찾기 위하여 반복기법을 사용하였으며, 열화 요소는 매 반복 단계마다 부분적으로 복원된 고해상도 영상으로부터 이용하였다. 제안된 방식의 성능을 실험 결과를 통해 확인할 수 있었다.

• 대한원격탐사학회지
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• 제24권1호
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• pp.79-89
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• 2008

Irregular temporal sampling is a common feature of geophysical and biological time series in remote sensing. This study proposes an on-line system for reconstructing observation image series contaminated by noises resulted from mechanical problems or sensing environmental condition. There is also a high likelihood that during the data acquisition periods the target site corresponding to any given pixel may be covered by fog or cloud, thereby resulting in bad or missing observation. The surface parameters associated with the land are usually dependent on the climate, and many physical processes that are displayed in the image sensed from the land then exhibit temporal variation with seasonal periodicity. A feedback system proposed in this study reconstructs a sequence of images remotely sensed from the land surface having the physical processes with seasonal periodicity. The harmonic model is used to track seasonal variation through time, and a Gibbs random field (GRF) is used to represent the spatial dependency of digital image processes. The experimental results of this simulation study show the potentiality of the proposed system to reconstruct the image series observed by imperfect sensing technology from the environment which are frequently influenced by bad weather. This study provides fundamental information on the elements of the proposed system for right usage in application.

• 한국방사선학회논문지
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• 제17권7호
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• pp.1025-1031
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• 2023

저선량흉부 CT (Low Dose chest CT, LDCT)에서 Scout 관전압을 변화시키고 scan parameter인 자동노출제어장치(Auto Exposure Control, AEC)와 적응식 반복재구성기법(Adaptive Statistical Iterative Reconstruction, ASIR)등을 적용하여 최적의 프로토콜을 찾음으로써 방사선 피폭선량과 화질을 평가하고자 하였다. Scout 관전압을 80, 100, 120, 140 kV로 변화시키며 LDCT 프로토콜로 5회 반복 측정 후 선량을 비교하기 위해 장비에서 제공된 Dose report를 이용하여 연구 목적에 적합한 관전압을 선택하였다. 120 kV, 30 mAs의 조건으로 기본 LDCT 촬영한 후, 이 조건에 ASIR 50%를 적용하였으며 신호대잡음비와 대조도대잡음비를 평가하기 위해 배경의 노이즈를 측정하였다. 선량 비교를 위해 장비에서 제공되는 CTDI_vol과 선량길이곱(Dose length product, DLP)를 식을 이용하여 비교 분석하였다. 그 결과 S140 + LDCT + ASIR 50 + AEC를 적용한 프로토콜에서 고식적인 LCDT보다 방사선 피폭선량을 감소시키고 영상의 질을 향상시켰으며 최적의 프로토콜을 얻을 수 있었으며 LDCT는 매 검사 시 필요 이상의 피폭선량이 우려되기 때문에 적절한 Parameter를 적용하는 것이 중요하며, 향후 LDCT를 이용한 건강검진에서 국민의 건강에 이바지 하는데 긍정적인 요인으로 작용될 것으로 사료된다.

• International Journal of Control, Automation, and Systems
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• 제3권4호
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• pp.612-619
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• 2005

A robust adaptive speed sensorless induction motor direct torque control (DTC) using a neural network (NN) is presented in this paper. The inherent lumped uncertainties of the induction motor DTC system such as parametric uncertainty, external load disturbance and unmodeled dynamics are approximated by the NN. An additional robust control term is introduced to compensate for the reconstruction error. A control law and adaptive laws for the weights in the NN, as well as the bounding constant of the lumped uncertainties are established so that the whole closed-loop system is stable in the sense of Lyapunov. The effect of the speed estimation error is analyzed, and the stability proof of the control system is also proved. Experimental results as well as computer simulations are presented to show the validity and efficiency of the proposed system.

• 한국멀티미디어학회논문지
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• 제19권2호
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• pp.271-279
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• 2016

The recent development of the HD (High Definition) and UHD (Ultra High Definition) technology allowed the growth of 3D contents market. Yet the majority of the 3D contents in the market are strictly for 6.5 cm inter-ocular distance, causing various visual discomforts for the viewers who have different inter-ocular distance. Moreover, because the 3D contents are created for a fixed viewing distance, the change of the viewing distances when watching 3D contents can also cause visual conflicts. To solve this problem, we devised techniques that consider the environmental information of the viewer watching 3D contents. By analyzing the relationship between viewing distance, inter-ocular distance, and perceived depth, we created an adaptive content viewing system that reflects the viewer's environment to minimize any conflicts in watching 3D contents. From our experiments, we found that the performance of our adaptive content viewing system was reasonable.

• 제어로봇시스템학회논문지
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• 제12권3호
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• pp.233-240
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• 2006

This paper presents the robust control method using a self recurrent wavelet neural network (SRWNN) via adaptive backstepping design technique for stable walking of biped robots with unknown model uncertainties. The SRWNN, which has the properties such as fast convergence and simple structure, is used as the uncertainty observer of the biped robots. The adaptation laws for weights of the SRWNN and reconstruction error compensator are induced from the Lyapunov stability theorem, which are used for on-line controlling biped robots. Computer simulations of a five-link biped robot with unknown model uncertainties verify the validity of the proposed control system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1987년도 전기.전자공학 학술대회 논문집(II)
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• pp.1422-1426
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• 1987

In this paper, an adaptive DPCM scheme is presented for encoding monochrome images with easy hardware implementation at a transmission rate of exactly 1 bit/pel. The system is mainly composed of a compensated mean predictor and an adaptive two-level quantizer with backward estimation. In this system, the predictor is a sort of two-dimensional ARMA predictor in which a moving-average part is added to the conventional mean predictor. The quantizer adapts to the local statistics of its input without overhead information. To reduce annoying granular noise in the reconstructed image, Lee filter is used after reconstruction in the receiver.

• Journal of Positioning, Navigation, and Timing
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• 제8권4호
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• pp.193-200
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• 2019

The auto-encoder network which is a good candidate to handle the modeling of the signal strength attenuation is designed for denoising and compensating the distortion of the received data. It provides a non-linear mapping function by iteratively learning the encoder and the decoder. The encoder is the non-linear mapping function, and the decoder demands accurate data reconstruction from the representation generated by the encoder. In addition, the adaptive network width which supports the automatic generation of new hidden nodes and pruning of inconsequential nodes is also implemented in the proposed algorithm for increasing the efficiency of the algorithm. Simulation results show that the proposed method can improve the neural network training surface to achieve the highest possible accuracy of the signal modeling compared with the conventional modeling method.